Publications

Nicholson, G. et al. (2022) “Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework”, Nature Microbiology, 7(1), pp. 97–107.

Barrett, B. et al. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.

Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of Machine Learning Research, pp. 1676–1685.

Reichelt, T., Ong, L. and Rainforth, T. (2022) “Rethinking Variational Inference for Probabilistic Programs with Stochastic Support”, in Advances in Neural Information Processing Systems.

Kossen, J. et al. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.

Miao, N. et al. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in ICLR 2022 - 10th International Conference on Learning Representations.

Reichelt, T. et al. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 1676–1685.

Rudner, T. et al. (2022) “Tractable Function-Space Variational Inference in Bayesian Neural Networks”, in Advances in Neural Information Processing Systems.

Ghalebikesabi, S. et al. (2022) “Mitigating Statistical Bias within Differentially Private Synthetic Data”, in Proceedings of Machine Learning Research, pp. 685–695.